import numpy as np
import torch
from torch.autograd import Variable
from .get_nets import PNet, RNet, ONet
from .box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from .first_stage import run_first_stage

class FaceDetector(object):

    def __init__(self, device):
        
        self.device = device
        
        self.pnet = PNet().to(self.device)
        self.rnet = RNet().to(self.device)
        self.onet = ONet().to(self.device)
        self.onet.eval()  # Onet has dropout layer.
    


    def detect(self, image, min_face_size=20.0,
                     thresholds=[0.6, 0.7, 0.8],
                     nms_thresholds=[0.7, 0.7, 0.7]):
        """
        Arguments:
            image: an instance of PIL.Image.
            min_face_size: a float number.
            thresholds: a list of length 3.
            nms_thresholds: a list of length 3.

        Returns:
            two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
            bounding boxes and facial landmarks.
        """
        with torch.no_grad():

            # BUILD AN IMAGE PYRAMID
            width, height = image.size
            min_length = min(height, width)

            min_detection_size = 12
            factor = 0.707  # sqrt(0.5)

            # scales for scaling the image
            scales = []

            # scales the image so that
            # minimum size that we can detect equals to
            # minimum face size that we want to detect
            m = min_detection_size/min_face_size
            min_length *= m

            factor_count = 0
            while min_length > min_detection_size:
                scales.append(m*factor**factor_count)
                min_length *= factor
                factor_count += 1

            # STAGE 1

            # it will be returned
            bounding_boxes = []

            # run P-Net on different scales
            for s in scales:
                boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0], device=self.device)
                bounding_boxes.append(boxes)

            # collect boxes (and offsets, and scores) from different scales
            bounding_boxes = [i for i in bounding_boxes if i is not None]
            if len(bounding_boxes)== 0: return [], []
            bounding_boxes = np.vstack(bounding_boxes)

            keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
            bounding_boxes = bounding_boxes[keep]

            # use offsets predicted by pnet to transform bounding boxes
            bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
            # shape [n_boxes, 5]

            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

            # STAGE 2
            img_boxes = get_image_boxes(bounding_boxes, image, size=24)
            
            img_boxes = Variable(torch.FloatTensor(img_boxes))
            output = self.rnet(img_boxes.to(self.device))
            offsets = output[0].data.cpu().numpy()  # shape [n_boxes, 4]
            probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

            keep = np.where(probs[:, 1] > thresholds[1])[0]
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
            offsets = offsets[keep]

            keep = nms(bounding_boxes, nms_thresholds[1])
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

            # STAGE 3

            img_boxes = get_image_boxes(bounding_boxes, image, size=48)
            if len(img_boxes) == 0: 
                return [], []
            img_boxes = Variable(torch.FloatTensor(img_boxes))
            output = self.onet(img_boxes.to(self.device))
            landmarks = output[0].data.cpu().numpy()  # shape [n_boxes, 10]
            offsets = output[1].data.cpu().numpy()  # shape [n_boxes, 4]
            probs = output[2].data.cpu().numpy()  # shape [n_boxes, 2]

            keep = np.where(probs[:, 1] > thresholds[2])[0]
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
            offsets = offsets[keep]
            landmarks = landmarks[keep]

            # compute landmark points
            width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
            height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
            xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
            landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1)*landmarks[:, 0:5]
            landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1)*landmarks[:, 5:10]

            bounding_boxes = calibrate_box(bounding_boxes, offsets)
            keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
            bounding_boxes = bounding_boxes[keep]
            landmarks = landmarks[keep]

        return bounding_boxes, landmarks
